{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import date, timedelta\n",
    "from scipy.stats import skew, kurtosis\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras.models import *\n",
    "from tensorflow.keras.optimizers import *\n",
    "from tensorflow.keras.callbacks import *\n",
    "from tensorflow.keras import backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "### IMPORT SPEKTRAL CLASSES ###\n",
    "\n",
    "from spektral_utilities import *\n",
    "from spektral_gcn import GraphConv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(913000, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>store</th>\n",
       "      <th>item</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  store  item  sales\n",
       "0 2013-01-01      1     1     13\n",
       "1 2013-01-02      1     1     11\n",
       "2 2013-01-03      1     1     14\n",
       "3 2013-01-04      1     1     13\n",
       "4 2013-01-05      1     1     10"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### READ DATA ###\n",
    "\n",
    "df = pd.read_csv('sales_train.csv.zip')\n",
    "df['date'] = pd.to_datetime(df['date'])\n",
    "\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 1826)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>date</th>\n",
       "      <th>2013-01-01</th>\n",
       "      <th>2013-01-02</th>\n",
       "      <th>2013-01-03</th>\n",
       "      <th>2013-01-04</th>\n",
       "      <th>2013-01-05</th>\n",
       "      <th>2013-01-06</th>\n",
       "      <th>2013-01-07</th>\n",
       "      <th>2013-01-08</th>\n",
       "      <th>2013-01-09</th>\n",
       "      <th>2013-01-10</th>\n",
       "      <th>...</th>\n",
       "      <th>2017-12-22</th>\n",
       "      <th>2017-12-23</th>\n",
       "      <th>2017-12-24</th>\n",
       "      <th>2017-12-25</th>\n",
       "      <th>2017-12-26</th>\n",
       "      <th>2017-12-27</th>\n",
       "      <th>2017-12-28</th>\n",
       "      <th>2017-12-29</th>\n",
       "      <th>2017-12-30</th>\n",
       "      <th>2017-12-31</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>10_1</th>\n",
       "      <td>37.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>...</td>\n",
       "      <td>60.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10_10</th>\n",
       "      <td>45.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>...</td>\n",
       "      <td>64.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10_2</th>\n",
       "      <td>51.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>...</td>\n",
       "      <td>86.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10_3</th>\n",
       "      <td>38.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>...</td>\n",
       "      <td>83.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10_4</th>\n",
       "      <td>30.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>...</td>\n",
       "      <td>75.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1826 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "date   2013-01-01  2013-01-02  2013-01-03  2013-01-04  2013-01-05  2013-01-06  \\\n",
       "id                                                                              \n",
       "10_1         37.0        34.0        32.0        45.0        35.0        54.0   \n",
       "10_10        45.0        50.0        53.0        51.0        54.0        54.0   \n",
       "10_2         51.0        56.0        46.0        56.0        53.0        68.0   \n",
       "10_3         38.0        60.0        50.0        46.0        46.0        52.0   \n",
       "10_4         30.0        29.0        37.0        45.0        52.0        58.0   \n",
       "\n",
       "date   2013-01-07  2013-01-08  2013-01-09  2013-01-10  ...  2017-12-22  \\\n",
       "id                                                     ...               \n",
       "10_1         37.0        37.0        32.0        36.0  ...        60.0   \n",
       "10_10        54.0        40.0        50.0        53.0  ...        64.0   \n",
       "10_2         37.0        47.0        65.0        61.0  ...        86.0   \n",
       "10_3         40.0        41.0        32.0        38.0  ...        83.0   \n",
       "10_4         29.0        44.0        53.0        39.0  ...        75.0   \n",
       "\n",
       "date   2017-12-23  2017-12-24  2017-12-25  2017-12-26  2017-12-27  2017-12-28  \\\n",
       "id                                                                              \n",
       "10_1         67.0        88.0        42.0        50.0        55.0        63.0   \n",
       "10_10        74.0        66.0        61.0        53.0        72.0        81.0   \n",
       "10_2        114.0        84.0        66.0        69.0        63.0        78.0   \n",
       "10_3         71.0       101.0        45.0        61.0        51.0        56.0   \n",
       "10_4         64.0        86.0        56.0        54.0        57.0        53.0   \n",
       "\n",
       "date   2017-12-29  2017-12-30  2017-12-31  \n",
       "id                                         \n",
       "10_1         56.0        78.0        74.0  \n",
       "10_10        69.0        86.0        67.0  \n",
       "10_2         76.0        77.0       116.0  \n",
       "10_3         61.0        72.0        68.0  \n",
       "10_4         65.0        74.0        69.0  \n",
       "\n",
       "[5 rows x 1826 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### SWITCH DATA FROM VERTICAL TO HORIZONTAL FORMAT ###\n",
    "\n",
    "unstaked_df = df.copy()\n",
    "unstaked_df['id'] = df['item'].astype(str)+'_'+df['store'].astype(str)\n",
    "unstaked_df.set_index(['id','date'], inplace=True)\n",
    "unstaked_df.drop(['store','item'], axis=1, inplace=True)\n",
    "unstaked_df = unstaked_df.astype(float).unstack()\n",
    "unstaked_df.columns = unstaked_df.columns.get_level_values(1)\n",
    "\n",
    "print(unstaked_df.shape)\n",
    "unstaked_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "### UTILITY FUNCTIONS FOR FEATURE ENGINEERING ###\n",
    "\n",
    "sequence_length = 14\n",
    "\n",
    "\n",
    "\n",
    "def get_timespan(df, today, days):    \n",
    "    df = df[pd.date_range(today - timedelta(days=days), \n",
    "            periods=days, freq='D')] # day - n_days <= dates < day    \n",
    "    return df\n",
    "\n",
    "def create_features(df, today, seq_len):\n",
    "    \n",
    "    all_sequence = get_timespan(df, today, seq_len).values\n",
    "    \n",
    "    group_store = all_sequence.reshape((-1, 10, seq_len))\n",
    "    \n",
    "    store_corr = np.stack([np.corrcoef(i) for i in group_store], axis=0)\n",
    "    \n",
    "    store_features = np.stack([\n",
    "              group_store.mean(axis=2),\n",
    "              group_store[:,:,int(sequence_length/2):].mean(axis=2),\n",
    "              group_store.std(axis=2),\n",
    "              group_store[:,:,int(sequence_length/2):].std(axis=2),\n",
    "              skew(group_store, axis=2),\n",
    "              kurtosis(group_store, axis=2),\n",
    "              np.apply_along_axis(lambda x: np.polyfit(np.arange(0, sequence_length), x, 1)[0], 2, group_store)\n",
    "            ], axis=1)\n",
    "    \n",
    "    group_store = np.transpose(group_store, (0,2,1))\n",
    "    store_features = np.transpose(store_features, (0,2,1))\n",
    "    \n",
    "    return group_store, store_corr, store_features\n",
    "\n",
    "def create_label(df, today):\n",
    "    \n",
    "    y = df[today].values\n",
    "    \n",
    "    return y.reshape((-1, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x17b51859c88>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### PLOT A SEQUENCE OF SALES FOR ITEM 10 IN ALL STORES ###\n",
    "\n",
    "sequence = get_timespan(unstaked_df, date(2017,11,1), 30)\n",
    "sequence.head(10).T.plot(figsize=(14,5))\n",
    "plt.ylabel('sales')\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "### DEFINE TRAIN, VALID, TEST DATES ###\n",
    "\n",
    "train_date = date(2013, 1, 1)\n",
    "valid_date = date(2015, 1, 1)\n",
    "test_date = date(2016, 1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 717/717 [00:42<00:00, 16.97it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(35850, 14, 10) (35850, 10, 10) (35850, 10, 7) (35850, 10)\n"
     ]
    }
   ],
   "source": [
    "### CREATE TRAIN FEATURES ###\n",
    "\n",
    "X_seq, X_cor, X_feat, y = [], [], [], []\n",
    "\n",
    "for d in tqdm(pd.date_range(train_date+timedelta(days=sequence_length), valid_date)):\n",
    "    seq_, corr_, feat_ = create_features(unstaked_df, d, sequence_length)\n",
    "    y_ = create_label(unstaked_df, d)\n",
    "    X_seq.append(seq_), X_cor.append(corr_), X_feat.append(feat_), y.append(y_)\n",
    "    \n",
    "X_train_seq = np.concatenate(X_seq, axis=0).astype('float16')\n",
    "X_train_cor = np.concatenate(X_cor, axis=0).astype('float16')\n",
    "X_train_feat = np.concatenate(X_feat, axis=0).astype('float16')\n",
    "y_train = np.concatenate(y, axis=0).astype('float16')\n",
    "\n",
    "print(X_train_seq.shape, X_train_cor.shape, X_train_feat.shape, y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 352/352 [00:59<00:00,  5.95it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17600, 14, 10) (17600, 10, 10) (17600, 10, 7) (17600, 10)\n"
     ]
    }
   ],
   "source": [
    "### CREATE VALID FEATURES ###\n",
    "\n",
    "X_seq, X_cor, X_feat, y = [], [], [], []\n",
    "\n",
    "for d in tqdm(pd.date_range(valid_date+timedelta(days=sequence_length), test_date)):\n",
    "    seq_, corr_, feat_ = create_features(unstaked_df, d, sequence_length)\n",
    "    y_ = create_label(unstaked_df, d)\n",
    "    X_seq.append(seq_), X_cor.append(corr_), X_feat.append(feat_), y.append(y_)\n",
    "    \n",
    "X_valid_seq = np.concatenate(X_seq, axis=0).astype('float16')\n",
    "X_valid_cor = np.concatenate(X_cor, axis=0).astype('float16')\n",
    "X_valid_feat = np.concatenate(X_feat, axis=0).astype('float16')\n",
    "y_valid = np.concatenate(y, axis=0).astype('float16')\n",
    "\n",
    "print(X_valid_seq.shape, X_valid_cor.shape, X_valid_feat.shape, y_valid.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 352/352 [00:50<00:00,  6.96it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17600, 14, 10) (17600, 10, 10) (17600, 10, 7) (17600, 10)\n"
     ]
    }
   ],
   "source": [
    "### CREATE TEST FEATURES ###\n",
    "\n",
    "X_seq, X_cor, X_feat, y = [], [], [], []\n",
    "\n",
    "for d in tqdm(pd.date_range(test_date+timedelta(days=sequence_length), date(2016,12,31))):\n",
    "    seq_, corr_, feat_ = create_features(unstaked_df, d, sequence_length)\n",
    "    y_ = create_label(unstaked_df, d)\n",
    "    X_seq.append(seq_), X_cor.append(corr_), X_feat.append(feat_), y.append(y_)\n",
    "    \n",
    "X_test_seq = np.concatenate(X_seq, axis=0).astype('float16')\n",
    "X_test_cor = np.concatenate(X_cor, axis=0).astype('float16')\n",
    "X_test_feat = np.concatenate(X_feat, axis=0).astype('float16')\n",
    "y_test = np.concatenate(y, axis=0).astype('float16')\n",
    "\n",
    "print(X_test_seq.shape, X_test_cor.shape, X_test_feat.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "### SCALE SEQUENCES ###\n",
    "\n",
    "scaler_seq = StandardScaler()\n",
    "scaler_feat = StandardScaler()\n",
    "\n",
    "X_train_seq = scaler_seq.fit_transform(X_train_seq.reshape(-1,10)).reshape(X_train_seq.shape)\n",
    "X_valid_seq = scaler_seq.transform(X_valid_seq.reshape(-1,10)).reshape(X_valid_seq.shape)\n",
    "X_test_seq = scaler_seq.transform(X_test_seq.reshape(-1,10)).reshape(X_test_seq.shape)\n",
    "\n",
    "y_train = scaler_seq.transform(y_train)\n",
    "y_valid = scaler_seq.transform(y_valid)\n",
    "y_test = scaler_seq.transform(y_test)\n",
    "\n",
    "X_train_feat = scaler_feat.fit_transform(X_train_feat.reshape(-1,10)).reshape(X_train_feat.shape)\n",
    "X_valid_feat = scaler_feat.transform(X_valid_feat.reshape(-1,10)).reshape(X_valid_feat.shape)\n",
    "X_test_feat = scaler_feat.transform(X_test_feat.reshape(-1,10)).reshape(X_test_feat.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "### OBTAIN LAPLACIANS FROM CORRELATIONS ###\n",
    "\n",
    "X_train_lap = localpooling_filter(1 - np.abs(X_train_cor))\n",
    "X_valid_lap = localpooling_filter(1 - np.abs(X_valid_cor))\n",
    "X_test_lap = localpooling_filter(1 - np.abs(X_test_cor))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_seed(seed):\n",
    "    \n",
    "    tf.random.set_seed(seed)\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    random.seed(seed)\n",
    "\n",
    "\n",
    "def get_model():\n",
    "    \n",
    "    set_seed(33)\n",
    "\n",
    "    opt = Adam(lr=0.001)\n",
    "\n",
    "    inp_seq = Input((sequence_length, 10))\n",
    "    inp_lap = Input((10, 10))\n",
    "    inp_feat = Input((10, X_train_feat.shape[-1]))\n",
    "\n",
    "    x = GraphConv(32, activation='relu')([inp_feat, inp_lap])\n",
    "    x = GraphConv(16, activation='relu')([x, inp_lap])\n",
    "    x = Flatten()(x)\n",
    "\n",
    "    xx = LSTM(128, activation='relu', return_sequences=True)(inp_seq)\n",
    "    xx = LSTM(32, activation='relu')(xx)\n",
    "\n",
    "    x = Concatenate()([x,xx])\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Dropout(0.5)(x)\n",
    "    x = Dense(128, activation='relu')(x)\n",
    "    x = Dense(32, activation='relu')(x)\n",
    "    x = Dropout(0.3)(x)\n",
    "    out = Dense(1)(x)\n",
    "\n",
    "    model = Model([inp_seq, inp_lap, inp_feat], out)\n",
    "    model.compile(optimizer=opt, loss='mse', \n",
    "                  metrics=[tf.keras.metrics.RootMeanSquaredError()])\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_3 (InputLayer)            [(None, 10, 7)]      0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_2 (InputLayer)            [(None, 10, 10)]     0                                            \n",
      "__________________________________________________________________________________________________\n",
      "graph_conv (GraphConv)          (None, 10, 32)       256         input_3[0][0]                    \n",
      "                                                                 input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "input_1 (InputLayer)            [(None, 14, 10)]     0                                            \n",
      "__________________________________________________________________________________________________\n",
      "graph_conv_1 (GraphConv)        (None, 10, 16)       528         graph_conv[0][0]                 \n",
      "                                                                 input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "lstm (LSTM)                     (None, 14, 128)      71168       input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "flatten (Flatten)               (None, 160)          0           graph_conv_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "lstm_1 (LSTM)                   (None, 32)           20608       lstm[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 192)          0           flatten[0][0]                    \n",
      "                                                                 lstm_1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 192)          768         concatenate[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 192)          0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 128)          24704       dropout[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 32)           4128        dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 32)           0           dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 1)            33          dropout_1[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 122,193\n",
      "Trainable params: 121,809\n",
      "Non-trainable params: 384\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = get_model()\n",
    "model.summary() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------- store 0 -------\n",
      "Epoch 1/100\n",
      "141/141 - 14s - loss: 0.3320 - root_mean_squared_error: 0.5775 - val_loss: 0.7862 - val_root_mean_squared_error: 0.8875\n",
      "Epoch 2/100\n",
      "141/141 - 11s - loss: 0.2503 - root_mean_squared_error: 0.5007 - val_loss: 0.7250 - val_root_mean_squared_error: 0.8524\n",
      "Epoch 3/100\n",
      "141/141 - 11s - loss: 0.2385 - root_mean_squared_error: 0.4884 - val_loss: 0.5867 - val_root_mean_squared_error: 0.7668\n",
      "Epoch 4/100\n",
      "141/141 - 11s - loss: 0.2285 - root_mean_squared_error: 0.4772 - val_loss: 0.6818 - val_root_mean_squared_error: 0.8263\n",
      "Epoch 5/100\n",
      "141/141 - 11s - loss: 0.2078 - root_mean_squared_error: 0.4539 - val_loss: 1.2399 - val_root_mean_squared_error: 1.1146\n",
      "Epoch 6/100\n",
      "141/141 - 11s - loss: 0.1833 - root_mean_squared_error: 0.4268 - val_loss: 0.7841 - val_root_mean_squared_error: 0.8861\n",
      "Epoch 7/100\n",
      "141/141 - 11s - loss: 0.1754 - root_mean_squared_error: 0.4156 - val_loss: 0.4966 - val_root_mean_squared_error: 0.7054\n",
      "Epoch 8/100\n",
      "141/141 - 11s - loss: 0.1664 - root_mean_squared_error: 0.4084 - val_loss: 0.4827 - val_root_mean_squared_error: 0.6955\n",
      "Epoch 9/100\n",
      "141/141 - 11s - loss: 0.1618 - root_mean_squared_error: 0.4015 - val_loss: 0.5729 - val_root_mean_squared_error: 0.7577\n",
      "Epoch 10/100\n",
      "141/141 - 9s - loss: 0.1607 - root_mean_squared_error: 0.3995 - val_loss: 0.3445 - val_root_mean_squared_error: 0.5876\n",
      "Epoch 11/100\n",
      "141/141 - 8s - loss: 0.1633 - root_mean_squared_error: 0.3974 - val_loss: 0.3691 - val_root_mean_squared_error: 0.6082\n",
      "Epoch 12/100\n",
      "141/141 - 7s - loss: 0.1588 - root_mean_squared_error: 0.3983 - val_loss: 0.4446 - val_root_mean_squared_error: 0.6675\n",
      "Epoch 13/100\n",
      "141/141 - 7s - loss: 0.1568 - root_mean_squared_error: 0.3943 - val_loss: 0.3961 - val_root_mean_squared_error: 0.6301\n",
      "Epoch 14/100\n",
      "141/141 - 10s - loss: 0.1572 - root_mean_squared_error: 0.3964 - val_loss: 0.4635 - val_root_mean_squared_error: 0.6816\n",
      "Epoch 15/100\n",
      "Restoring model weights from the end of the best epoch.\n",
      "141/141 - 10s - loss: 0.1574 - root_mean_squared_error: 0.3928 - val_loss: 0.4197 - val_root_mean_squared_error: 0.6486\n",
      "Epoch 00015: early stopping\n",
      "------- store 1 -------\n",
      "Epoch 1/100\n",
      "141/141 - 13s - loss: 0.3459 - root_mean_squared_error: 0.5880 - val_loss: 0.9034 - val_root_mean_squared_error: 0.9514\n",
      "Epoch 2/100\n",
      "141/141 - 11s - loss: 0.2427 - root_mean_squared_error: 0.4918 - val_loss: 0.6920 - val_root_mean_squared_error: 0.8327\n",
      "Epoch 3/100\n",
      "141/141 - 12s - loss: 0.2212 - root_mean_squared_error: 0.4702 - val_loss: 0.5973 - val_root_mean_squared_error: 0.7738\n",
      "Epoch 4/100\n",
      "141/141 - 12s - loss: 0.2071 - root_mean_squared_error: 0.4517 - val_loss: 0.5714 - val_root_mean_squared_error: 0.7568\n",
      "Epoch 5/100\n",
      "141/141 - 11s - loss: 0.1814 - root_mean_squared_error: 0.4248 - val_loss: 0.6838 - val_root_mean_squared_error: 0.8278\n",
      "Epoch 6/100\n",
      "141/141 - 11s - loss: 0.1639 - root_mean_squared_error: 0.4038 - val_loss: 0.3678 - val_root_mean_squared_error: 0.6072\n",
      "Epoch 7/100\n",
      "141/141 - 12s - loss: 0.1581 - root_mean_squared_error: 0.3942 - val_loss: 0.4518 - val_root_mean_squared_error: 0.6730\n",
      "Epoch 8/100\n",
      "141/141 - 11s - loss: 0.1562 - root_mean_squared_error: 0.3956 - val_loss: 0.4432 - val_root_mean_squared_error: 0.6665\n",
      "Epoch 9/100\n",
      "141/141 - 11s - loss: 0.1497 - root_mean_squared_error: 0.3874 - val_loss: 0.3196 - val_root_mean_squared_error: 0.5659\n",
      "Epoch 10/100\n",
      "141/141 - 11s - loss: 0.1453 - root_mean_squared_error: 0.3811 - val_loss: 0.4167 - val_root_mean_squared_error: 0.6463\n",
      "Epoch 11/100\n",
      "141/141 - 11s - loss: 0.1472 - root_mean_squared_error: 0.3783 - val_loss: 0.4039 - val_root_mean_squared_error: 0.6362\n",
      "Epoch 12/100\n",
      "141/141 - 11s - loss: 0.1449 - root_mean_squared_error: 0.3796 - val_loss: 0.4253 - val_root_mean_squared_error: 0.6530\n",
      "Epoch 13/100\n",
      "141/141 - 11s - loss: 0.1421 - root_mean_squared_error: 0.3770 - val_loss: 0.3695 - val_root_mean_squared_error: 0.6087\n",
      "Epoch 14/100\n",
      "Restoring model weights from the end of the best epoch.\n",
      "141/141 - 11s - loss: 0.1431 - root_mean_squared_error: 0.3780 - val_loss: 0.5075 - val_root_mean_squared_error: 0.7132\n",
      "Epoch 00014: early stopping\n",
      "------- store 2 -------\n",
      "Epoch 1/100\n",
      "141/141 - 8s - loss: 0.3037 - root_mean_squared_error: 0.5522 - val_loss: 1.1585 - val_root_mean_squared_error: 1.0774\n",
      "Epoch 2/100\n",
      "141/141 - 7s - loss: 0.2210 - root_mean_squared_error: 0.4690 - val_loss: 0.9484 - val_root_mean_squared_error: 0.9748\n",
      "Epoch 3/100\n",
      "141/141 - 10s - loss: 0.2069 - root_mean_squared_error: 0.4546 - val_loss: 0.4015 - val_root_mean_squared_error: 0.6341\n",
      "Epoch 4/100\n",
      "141/141 - 10s - loss: 0.1800 - root_mean_squared_error: 0.4232 - val_loss: 0.6716 - val_root_mean_squared_error: 0.8204\n",
      "Epoch 5/100\n",
      "141/141 - 11s - loss: 0.1511 - root_mean_squared_error: 0.3874 - val_loss: 0.6107 - val_root_mean_squared_error: 0.7822\n",
      "Epoch 6/100\n",
      "141/141 - 11s - loss: 0.1504 - root_mean_squared_error: 0.3864 - val_loss: 0.5582 - val_root_mean_squared_error: 0.7478\n",
      "Epoch 7/100\n",
      "141/141 - 11s - loss: 0.1461 - root_mean_squared_error: 0.3803 - val_loss: 0.4284 - val_root_mean_squared_error: 0.6553\n",
      "Epoch 8/100\n",
      "141/141 - 11s - loss: 0.1425 - root_mean_squared_error: 0.3784 - val_loss: 0.3924 - val_root_mean_squared_error: 0.6270\n",
      "Epoch 9/100\n",
      "141/141 - 11s - loss: 0.1377 - root_mean_squared_error: 0.3712 - val_loss: 0.5671 - val_root_mean_squared_error: 0.7538\n",
      "Epoch 10/100\n",
      "141/141 - 11s - loss: 0.1354 - root_mean_squared_error: 0.3671 - val_loss: 0.3615 - val_root_mean_squared_error: 0.6019\n",
      "Epoch 11/100\n",
      "141/141 - 11s - loss: 0.1420 - root_mean_squared_error: 0.3707 - val_loss: 0.4286 - val_root_mean_squared_error: 0.6553\n",
      "Epoch 12/100\n",
      "141/141 - 11s - loss: 0.1346 - root_mean_squared_error: 0.3661 - val_loss: 0.3444 - val_root_mean_squared_error: 0.5875\n",
      "Epoch 13/100\n",
      "141/141 - 11s - loss: 0.1326 - root_mean_squared_error: 0.3639 - val_loss: 0.3584 - val_root_mean_squared_error: 0.5994\n",
      "Epoch 14/100\n",
      "141/141 - 11s - loss: 0.1309 - root_mean_squared_error: 0.3612 - val_loss: 0.4288 - val_root_mean_squared_error: 0.6556\n",
      "Epoch 15/100\n",
      "141/141 - 11s - loss: 0.1294 - root_mean_squared_error: 0.3590 - val_loss: 0.4133 - val_root_mean_squared_error: 0.6436\n",
      "Epoch 16/100\n",
      "141/141 - 10s - loss: 0.1296 - root_mean_squared_error: 0.3595 - val_loss: 0.3274 - val_root_mean_squared_error: 0.5728\n",
      "Epoch 17/100\n",
      "141/141 - 11s - loss: 0.1298 - root_mean_squared_error: 0.3598 - val_loss: 0.4646 - val_root_mean_squared_error: 0.6824\n",
      "Epoch 18/100\n",
      "141/141 - 9s - loss: 0.1320 - root_mean_squared_error: 0.3601 - val_loss: 0.2846 - val_root_mean_squared_error: 0.5341\n",
      "Epoch 19/100\n",
      "141/141 - 8s - loss: 0.1258 - root_mean_squared_error: 0.3554 - val_loss: 0.4410 - val_root_mean_squared_error: 0.6648\n",
      "Epoch 20/100\n",
      "141/141 - 7s - loss: 0.1252 - root_mean_squared_error: 0.3535 - val_loss: 0.3935 - val_root_mean_squared_error: 0.6280\n",
      "Epoch 21/100\n",
      "141/141 - 7s - loss: 0.1288 - root_mean_squared_error: 0.3561 - val_loss: 0.3467 - val_root_mean_squared_error: 0.5895\n",
      "Epoch 22/100\n",
      "141/141 - 10s - loss: 0.1259 - root_mean_squared_error: 0.3544 - val_loss: 0.3840 - val_root_mean_squared_error: 0.6204\n",
      "Epoch 23/100\n",
      "Restoring model weights from the end of the best epoch.\n",
      "141/141 - 11s - loss: 0.1278 - root_mean_squared_error: 0.3555 - val_loss: 0.4264 - val_root_mean_squared_error: 0.6538\n",
      "Epoch 00023: early stopping\n",
      "------- store 3 -------\n",
      "Epoch 1/100\n",
      "141/141 - 13s - loss: 0.4398 - root_mean_squared_error: 0.6636 - val_loss: 1.0512 - val_root_mean_squared_error: 1.0263\n",
      "Epoch 2/100\n",
      "141/141 - 11s - loss: 0.2563 - root_mean_squared_error: 0.5049 - val_loss: 0.7942 - val_root_mean_squared_error: 0.8920\n",
      "Epoch 3/100\n",
      "141/141 - 11s - loss: 0.2294 - root_mean_squared_error: 0.4799 - val_loss: 0.6566 - val_root_mean_squared_error: 0.8112\n",
      "Epoch 4/100\n",
      "141/141 - 11s - loss: 0.2020 - root_mean_squared_error: 0.4494 - val_loss: 0.4809 - val_root_mean_squared_error: 0.6942\n",
      "Epoch 5/100\n",
      "141/141 - 11s - loss: 0.1858 - root_mean_squared_error: 0.4291 - val_loss: 0.5163 - val_root_mean_squared_error: 0.7193\n",
      "Epoch 6/100\n",
      "141/141 - 11s - loss: 0.1730 - root_mean_squared_error: 0.4146 - val_loss: 0.3779 - val_root_mean_squared_error: 0.6154\n",
      "Epoch 7/100\n",
      "141/141 - 13s - loss: 0.1659 - root_mean_squared_error: 0.4056 - val_loss: 0.4543 - val_root_mean_squared_error: 0.6748\n",
      "Epoch 8/100\n",
      "141/141 - 15s - loss: 0.1655 - root_mean_squared_error: 0.4069 - val_loss: 0.3056 - val_root_mean_squared_error: 0.5534\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/100\n",
      "141/141 - 15s - loss: 0.1580 - root_mean_squared_error: 0.3965 - val_loss: 0.3309 - val_root_mean_squared_error: 0.5759\n",
      "Epoch 10/100\n",
      "141/141 - 12s - loss: 0.1568 - root_mean_squared_error: 0.3956 - val_loss: 0.2981 - val_root_mean_squared_error: 0.5466\n",
      "Epoch 11/100\n",
      "141/141 - 12s - loss: 0.1582 - root_mean_squared_error: 0.3905 - val_loss: 0.3664 - val_root_mean_squared_error: 0.6060\n",
      "Epoch 12/100\n",
      "141/141 - 12s - loss: 0.1499 - root_mean_squared_error: 0.3869 - val_loss: 0.4016 - val_root_mean_squared_error: 0.6344\n",
      "Epoch 13/100\n",
      "141/141 - 12s - loss: 0.1467 - root_mean_squared_error: 0.3831 - val_loss: 0.3928 - val_root_mean_squared_error: 0.6274\n",
      "Epoch 14/100\n",
      "141/141 - 12s - loss: 0.1451 - root_mean_squared_error: 0.3811 - val_loss: 0.3978 - val_root_mean_squared_error: 0.6314\n",
      "Epoch 15/100\n",
      "Restoring model weights from the end of the best epoch.\n",
      "141/141 - 12s - loss: 0.1433 - root_mean_squared_error: 0.3756 - val_loss: 0.3390 - val_root_mean_squared_error: 0.5829\n",
      "Epoch 00015: early stopping\n",
      "------- store 4 -------\n",
      "Epoch 1/100\n",
      "141/141 - 8s - loss: 0.3115 - root_mean_squared_error: 0.5592 - val_loss: 0.9076 - val_root_mean_squared_error: 0.9537\n",
      "Epoch 2/100\n",
      "141/141 - 7s - loss: 0.2433 - root_mean_squared_error: 0.4918 - val_loss: 0.6301 - val_root_mean_squared_error: 0.7945\n",
      "Epoch 3/100\n",
      "141/141 - 11s - loss: 0.2256 - root_mean_squared_error: 0.4746 - val_loss: 0.6624 - val_root_mean_squared_error: 0.8148\n",
      "Epoch 4/100\n",
      "141/141 - 12s - loss: 0.2065 - root_mean_squared_error: 0.4527 - val_loss: 0.4759 - val_root_mean_squared_error: 0.6905\n",
      "Epoch 5/100\n",
      "141/141 - 11s - loss: 0.1838 - root_mean_squared_error: 0.4257 - val_loss: 0.6242 - val_root_mean_squared_error: 0.7908\n",
      "Epoch 6/100\n",
      "141/141 - 11s - loss: 0.1669 - root_mean_squared_error: 0.4074 - val_loss: 0.4557 - val_root_mean_squared_error: 0.6758\n",
      "Epoch 7/100\n",
      "141/141 - 12s - loss: 0.1618 - root_mean_squared_error: 0.3999 - val_loss: 0.5533 - val_root_mean_squared_error: 0.7447\n",
      "Epoch 8/100\n",
      "141/141 - 12s - loss: 0.1560 - root_mean_squared_error: 0.3955 - val_loss: 0.4005 - val_root_mean_squared_error: 0.6335\n",
      "Epoch 9/100\n",
      "141/141 - 12s - loss: 0.1539 - root_mean_squared_error: 0.3928 - val_loss: 0.3926 - val_root_mean_squared_error: 0.6273\n",
      "Epoch 10/100\n",
      "141/141 - 11s - loss: 0.1507 - root_mean_squared_error: 0.3891 - val_loss: 0.3793 - val_root_mean_squared_error: 0.6166\n",
      "Epoch 11/100\n",
      "141/141 - 11s - loss: 0.1564 - root_mean_squared_error: 0.3892 - val_loss: 0.3752 - val_root_mean_squared_error: 0.6132\n",
      "Epoch 12/100\n",
      "141/141 - 12s - loss: 0.1545 - root_mean_squared_error: 0.3928 - val_loss: 0.3601 - val_root_mean_squared_error: 0.6008\n",
      "Epoch 13/100\n",
      "141/141 - 12s - loss: 0.1510 - root_mean_squared_error: 0.3890 - val_loss: 0.3947 - val_root_mean_squared_error: 0.6290\n",
      "Epoch 14/100\n",
      "141/141 - 12s - loss: 0.1529 - root_mean_squared_error: 0.3907 - val_loss: 0.4419 - val_root_mean_squared_error: 0.6655\n",
      "Epoch 15/100\n",
      "141/141 - 11s - loss: 0.1543 - root_mean_squared_error: 0.3880 - val_loss: 0.3945 - val_root_mean_squared_error: 0.6288\n",
      "Epoch 16/100\n",
      "141/141 - 12s - loss: 0.1516 - root_mean_squared_error: 0.3874 - val_loss: 0.3425 - val_root_mean_squared_error: 0.5858\n",
      "Epoch 17/100\n",
      "141/141 - 12s - loss: 0.1505 - root_mean_squared_error: 0.3883 - val_loss: 0.3915 - val_root_mean_squared_error: 0.6264\n",
      "Epoch 18/100\n",
      "141/141 - 11s - loss: 0.1514 - root_mean_squared_error: 0.3885 - val_loss: 0.3371 - val_root_mean_squared_error: 0.5813\n",
      "Epoch 19/100\n",
      "141/141 - 8s - loss: 0.1452 - root_mean_squared_error: 0.3816 - val_loss: 0.3989 - val_root_mean_squared_error: 0.6323\n",
      "Epoch 20/100\n",
      "141/141 - 7s - loss: 0.1477 - root_mean_squared_error: 0.3832 - val_loss: 0.3947 - val_root_mean_squared_error: 0.6290\n",
      "Epoch 21/100\n",
      "141/141 - 7s - loss: 0.1532 - root_mean_squared_error: 0.3889 - val_loss: 0.4315 - val_root_mean_squared_error: 0.6577\n",
      "Epoch 22/100\n",
      "141/141 - 9s - loss: 0.1493 - root_mean_squared_error: 0.3861 - val_loss: 0.3811 - val_root_mean_squared_error: 0.6180\n",
      "Epoch 23/100\n",
      "Restoring model weights from the end of the best epoch.\n",
      "141/141 - 10s - loss: 0.1492 - root_mean_squared_error: 0.3871 - val_loss: 0.4400 - val_root_mean_squared_error: 0.6640\n",
      "Epoch 00023: early stopping\n",
      "------- store 5 -------\n",
      "Epoch 1/100\n",
      "141/141 - 15s - loss: 0.3640 - root_mean_squared_error: 0.6046 - val_loss: 0.8074 - val_root_mean_squared_error: 0.8993\n",
      "Epoch 2/100\n",
      "141/141 - 13s - loss: 0.2759 - root_mean_squared_error: 0.5248 - val_loss: 0.6735 - val_root_mean_squared_error: 0.8214\n",
      "Epoch 3/100\n",
      "141/141 - 10s - loss: 0.2600 - root_mean_squared_error: 0.5089 - val_loss: 0.5877 - val_root_mean_squared_error: 0.7674\n",
      "Epoch 4/100\n",
      "141/141 - 9s - loss: 0.2322 - root_mean_squared_error: 0.4812 - val_loss: 0.6096 - val_root_mean_squared_error: 0.7816\n",
      "Epoch 5/100\n",
      "141/141 - 7s - loss: 0.2154 - root_mean_squared_error: 0.4646 - val_loss: 0.6053 - val_root_mean_squared_error: 0.7788\n",
      "Epoch 6/100\n",
      "141/141 - 8s - loss: 0.1983 - root_mean_squared_error: 0.4444 - val_loss: 0.4549 - val_root_mean_squared_error: 0.6751\n",
      "Epoch 7/100\n",
      "141/141 - 10s - loss: 0.1918 - root_mean_squared_error: 0.4368 - val_loss: 0.5000 - val_root_mean_squared_error: 0.7079\n",
      "Epoch 8/100\n",
      "141/141 - 11s - loss: 0.1872 - root_mean_squared_error: 0.4326 - val_loss: 0.5007 - val_root_mean_squared_error: 0.7084\n",
      "Epoch 9/100\n",
      "141/141 - 12s - loss: 0.1848 - root_mean_squared_error: 0.4301 - val_loss: 0.5277 - val_root_mean_squared_error: 0.7272\n",
      "Epoch 10/100\n",
      "141/141 - 12s - loss: 0.1841 - root_mean_squared_error: 0.4277 - val_loss: 0.4451 - val_root_mean_squared_error: 0.6678\n",
      "Epoch 11/100\n",
      "141/141 - 12s - loss: 0.1833 - root_mean_squared_error: 0.4269 - val_loss: 0.3908 - val_root_mean_squared_error: 0.6257\n",
      "Epoch 12/100\n",
      "141/141 - 11s - loss: 0.1830 - root_mean_squared_error: 0.4275 - val_loss: 0.3986 - val_root_mean_squared_error: 0.6320\n",
      "Epoch 13/100\n",
      "141/141 - 11s - loss: 0.1768 - root_mean_squared_error: 0.4209 - val_loss: 0.4593 - val_root_mean_squared_error: 0.6784\n",
      "Epoch 14/100\n",
      "141/141 - 12s - loss: 0.1775 - root_mean_squared_error: 0.4204 - val_loss: 0.4745 - val_root_mean_squared_error: 0.6896\n",
      "Epoch 15/100\n",
      "141/141 - 12s - loss: 0.1763 - root_mean_squared_error: 0.4196 - val_loss: 0.5277 - val_root_mean_squared_error: 0.7272\n",
      "Epoch 16/100\n",
      "Restoring model weights from the end of the best epoch.\n",
      "141/141 - 12s - loss: 0.1775 - root_mean_squared_error: 0.4220 - val_loss: 0.4842 - val_root_mean_squared_error: 0.6966\n",
      "Epoch 00016: early stopping\n",
      "------- store 6 -------\n",
      "Epoch 1/100\n",
      "141/141 - 13s - loss: 0.3282 - root_mean_squared_error: 0.5735 - val_loss: 1.2050 - val_root_mean_squared_error: 1.0989\n",
      "Epoch 2/100\n",
      "141/141 - 11s - loss: 0.2596 - root_mean_squared_error: 0.5094 - val_loss: 0.4729 - val_root_mean_squared_error: 0.6880\n",
      "Epoch 3/100\n",
      "141/141 - 12s - loss: 0.2185 - root_mean_squared_error: 0.4682 - val_loss: 0.6709 - val_root_mean_squared_error: 0.8199\n",
      "Epoch 4/100\n",
      "141/141 - 15s - loss: 0.1991 - root_mean_squared_error: 0.4454 - val_loss: 0.6819 - val_root_mean_squared_error: 0.8268\n",
      "Epoch 5/100\n",
      "141/141 - 15s - loss: 0.1914 - root_mean_squared_error: 0.4342 - val_loss: 0.6487 - val_root_mean_squared_error: 0.8064\n",
      "Epoch 6/100\n",
      "141/141 - 12s - loss: 0.1859 - root_mean_squared_error: 0.4302 - val_loss: 0.4787 - val_root_mean_squared_error: 0.6927\n",
      "Epoch 7/100\n",
      "Restoring model weights from the end of the best epoch.\n",
      "141/141 - 12s - loss: 0.1799 - root_mean_squared_error: 0.4208 - val_loss: 0.6149 - val_root_mean_squared_error: 0.7851\n",
      "Epoch 00007: early stopping\n",
      "------- store 7 -------\n",
      "Epoch 1/100\n",
      "141/141 - 14s - loss: 0.3797 - root_mean_squared_error: 0.6150 - val_loss: 0.8725 - val_root_mean_squared_error: 0.9349\n",
      "Epoch 2/100\n",
      "141/141 - 11s - loss: 0.2781 - root_mean_squared_error: 0.5257 - val_loss: 0.6626 - val_root_mean_squared_error: 0.8147\n",
      "Epoch 3/100\n",
      "141/141 - 9s - loss: 0.2624 - root_mean_squared_error: 0.5120 - val_loss: 0.5668 - val_root_mean_squared_error: 0.7536\n",
      "Epoch 4/100\n",
      "141/141 - 8s - loss: 0.2363 - root_mean_squared_error: 0.4863 - val_loss: 0.5639 - val_root_mean_squared_error: 0.7517\n",
      "Epoch 5/100\n",
      "141/141 - 7s - loss: 0.2198 - root_mean_squared_error: 0.4687 - val_loss: 0.6733 - val_root_mean_squared_error: 0.8214\n",
      "Epoch 6/100\n",
      "141/141 - 8s - loss: 0.2129 - root_mean_squared_error: 0.4590 - val_loss: 0.4210 - val_root_mean_squared_error: 0.6494\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/100\n",
      "141/141 - 11s - loss: 0.2038 - root_mean_squared_error: 0.4508 - val_loss: 0.4937 - val_root_mean_squared_error: 0.7034\n",
      "Epoch 8/100\n",
      "141/141 - 11s - loss: 0.1959 - root_mean_squared_error: 0.4424 - val_loss: 0.5029 - val_root_mean_squared_error: 0.7098\n",
      "Epoch 9/100\n",
      "141/141 - 12s - loss: 0.1946 - root_mean_squared_error: 0.4410 - val_loss: 0.5382 - val_root_mean_squared_error: 0.7344\n",
      "Epoch 10/100\n",
      "141/141 - 11s - loss: 0.1910 - root_mean_squared_error: 0.4374 - val_loss: 0.4118 - val_root_mean_squared_error: 0.6424\n",
      "Epoch 11/100\n",
      "141/141 - 11s - loss: 0.1907 - root_mean_squared_error: 0.4345 - val_loss: 0.4740 - val_root_mean_squared_error: 0.6891\n",
      "Epoch 12/100\n",
      "141/141 - 11s - loss: 0.1879 - root_mean_squared_error: 0.4325 - val_loss: 0.4576 - val_root_mean_squared_error: 0.6772\n",
      "Epoch 13/100\n",
      "141/141 - 11s - loss: 0.1845 - root_mean_squared_error: 0.4295 - val_loss: 0.4486 - val_root_mean_squared_error: 0.6705\n",
      "Epoch 14/100\n",
      "141/141 - 11s - loss: 0.1839 - root_mean_squared_error: 0.4290 - val_loss: 0.5052 - val_root_mean_squared_error: 0.7115\n",
      "Epoch 15/100\n",
      "141/141 - 12s - loss: 0.1842 - root_mean_squared_error: 0.4268 - val_loss: 0.3713 - val_root_mean_squared_error: 0.6100\n",
      "Epoch 16/100\n",
      "141/141 - 12s - loss: 0.1811 - root_mean_squared_error: 0.4239 - val_loss: 0.3810 - val_root_mean_squared_error: 0.6178\n",
      "Epoch 17/100\n",
      "141/141 - 11s - loss: 0.1828 - root_mean_squared_error: 0.4269 - val_loss: 0.4082 - val_root_mean_squared_error: 0.6396\n",
      "Epoch 18/100\n",
      "141/141 - 11s - loss: 0.1830 - root_mean_squared_error: 0.4267 - val_loss: 0.4018 - val_root_mean_squared_error: 0.6345\n",
      "Epoch 19/100\n",
      "141/141 - 11s - loss: 0.1779 - root_mean_squared_error: 0.4213 - val_loss: 0.4324 - val_root_mean_squared_error: 0.6583\n",
      "Epoch 20/100\n",
      "141/141 - 11s - loss: 0.1763 - root_mean_squared_error: 0.4203 - val_loss: 0.3645 - val_root_mean_squared_error: 0.6043\n",
      "Epoch 21/100\n",
      "141/141 - 11s - loss: 0.1789 - root_mean_squared_error: 0.4213 - val_loss: 0.4529 - val_root_mean_squared_error: 0.6737\n",
      "Epoch 22/100\n",
      "141/141 - 10s - loss: 0.1811 - root_mean_squared_error: 0.4239 - val_loss: 0.5179 - val_root_mean_squared_error: 0.7204\n",
      "Epoch 23/100\n",
      "141/141 - 8s - loss: 0.1770 - root_mean_squared_error: 0.4212 - val_loss: 0.3833 - val_root_mean_squared_error: 0.6198\n",
      "Epoch 24/100\n",
      "141/141 - 7s - loss: 0.1768 - root_mean_squared_error: 0.4208 - val_loss: 0.3604 - val_root_mean_squared_error: 0.6010\n",
      "Epoch 25/100\n",
      "141/141 - 7s - loss: 0.1758 - root_mean_squared_error: 0.4200 - val_loss: 0.3676 - val_root_mean_squared_error: 0.6070\n",
      "Epoch 26/100\n",
      "141/141 - 10s - loss: 0.1763 - root_mean_squared_error: 0.4190 - val_loss: 0.4754 - val_root_mean_squared_error: 0.6902\n",
      "Epoch 27/100\n",
      "141/141 - 10s - loss: 0.1781 - root_mean_squared_error: 0.4219 - val_loss: 0.5459 - val_root_mean_squared_error: 0.7397\n",
      "Epoch 28/100\n",
      "141/141 - 11s - loss: 0.1763 - root_mean_squared_error: 0.4187 - val_loss: 0.4438 - val_root_mean_squared_error: 0.6669\n",
      "Epoch 29/100\n",
      "Restoring model weights from the end of the best epoch.\n",
      "141/141 - 11s - loss: 0.1756 - root_mean_squared_error: 0.4188 - val_loss: 0.4311 - val_root_mean_squared_error: 0.6573\n",
      "Epoch 00029: early stopping\n",
      "------- store 8 -------\n",
      "Epoch 1/100\n",
      "141/141 - 14s - loss: 0.3157 - root_mean_squared_error: 0.5630 - val_loss: 0.9084 - val_root_mean_squared_error: 0.9541\n",
      "Epoch 2/100\n",
      "141/141 - 9s - loss: 0.2244 - root_mean_squared_error: 0.4723 - val_loss: 0.7028 - val_root_mean_squared_error: 0.8392\n",
      "Epoch 3/100\n",
      "141/141 - 9s - loss: 0.2142 - root_mean_squared_error: 0.4622 - val_loss: 0.6154 - val_root_mean_squared_error: 0.7854\n",
      "Epoch 4/100\n",
      "141/141 - 7s - loss: 0.2041 - root_mean_squared_error: 0.4501 - val_loss: 0.5466 - val_root_mean_squared_error: 0.7401\n",
      "Epoch 5/100\n",
      "141/141 - 7s - loss: 0.1763 - root_mean_squared_error: 0.4176 - val_loss: 0.7087 - val_root_mean_squared_error: 0.8428\n",
      "Epoch 6/100\n",
      "141/141 - 7s - loss: 0.1594 - root_mean_squared_error: 0.3969 - val_loss: 0.3926 - val_root_mean_squared_error: 0.6272\n",
      "Epoch 7/100\n",
      "141/141 - 7s - loss: 0.1497 - root_mean_squared_error: 0.3840 - val_loss: 0.4402 - val_root_mean_squared_error: 0.6641\n",
      "Epoch 8/100\n",
      "141/141 - 7s - loss: 0.1509 - root_mean_squared_error: 0.3885 - val_loss: 0.3929 - val_root_mean_squared_error: 0.6275\n",
      "Epoch 9/100\n",
      "141/141 - 7s - loss: 0.1429 - root_mean_squared_error: 0.3781 - val_loss: 0.3570 - val_root_mean_squared_error: 0.5982\n",
      "Epoch 10/100\n",
      "141/141 - 7s - loss: 0.1391 - root_mean_squared_error: 0.3733 - val_loss: 0.2755 - val_root_mean_squared_error: 0.5255\n",
      "Epoch 11/100\n",
      "141/141 - 7s - loss: 0.1375 - root_mean_squared_error: 0.3637 - val_loss: 0.3466 - val_root_mean_squared_error: 0.5894\n",
      "Epoch 12/100\n",
      "141/141 - 7s - loss: 0.1356 - root_mean_squared_error: 0.3686 - val_loss: 0.4739 - val_root_mean_squared_error: 0.6891\n",
      "Epoch 13/100\n",
      "141/141 - 7s - loss: 0.1298 - root_mean_squared_error: 0.3600 - val_loss: 0.3296 - val_root_mean_squared_error: 0.5748\n",
      "Epoch 14/100\n",
      "141/141 - 7s - loss: 0.1290 - root_mean_squared_error: 0.3588 - val_loss: 0.4008 - val_root_mean_squared_error: 0.6338\n",
      "Epoch 15/100\n",
      "Restoring model weights from the end of the best epoch.\n",
      "141/141 - 7s - loss: 0.1303 - root_mean_squared_error: 0.3578 - val_loss: 0.3345 - val_root_mean_squared_error: 0.5791\n",
      "Epoch 00015: early stopping\n",
      "------- store 9 -------\n",
      "Epoch 1/100\n",
      "141/141 - 7s - loss: 0.3588 - root_mean_squared_error: 0.6002 - val_loss: 0.7938 - val_root_mean_squared_error: 0.8918\n",
      "Epoch 2/100\n",
      "141/141 - 7s - loss: 0.2503 - root_mean_squared_error: 0.4996 - val_loss: 0.7793 - val_root_mean_squared_error: 0.8838\n",
      "Epoch 3/100\n",
      "141/141 - 7s - loss: 0.2296 - root_mean_squared_error: 0.4794 - val_loss: 0.4283 - val_root_mean_squared_error: 0.6550\n",
      "Epoch 4/100\n",
      "141/141 - 7s - loss: 0.2221 - root_mean_squared_error: 0.4688 - val_loss: 0.5983 - val_root_mean_squared_error: 0.7744\n",
      "Epoch 5/100\n",
      "141/141 - 7s - loss: 0.2077 - root_mean_squared_error: 0.4545 - val_loss: 0.8620 - val_root_mean_squared_error: 0.9294\n",
      "Epoch 6/100\n",
      "141/141 - 7s - loss: 0.1848 - root_mean_squared_error: 0.4296 - val_loss: 0.6182 - val_root_mean_squared_error: 0.7871\n",
      "Epoch 7/100\n",
      "141/141 - 7s - loss: 0.1705 - root_mean_squared_error: 0.4116 - val_loss: 0.4964 - val_root_mean_squared_error: 0.7053\n",
      "Epoch 8/100\n",
      "Restoring model weights from the end of the best epoch.\n",
      "141/141 - 7s - loss: 0.1606 - root_mean_squared_error: 0.4015 - val_loss: 0.4683 - val_root_mean_squared_error: 0.6850\n",
      "Epoch 00008: early stopping\n"
     ]
    }
   ],
   "source": [
    "### TRAIN A MODEL FOR EACH STORES USING ALL THE DATA AVAILALBE FROM OTHER STORES ###\n",
    "\n",
    "pred_valid_all = np.zeros(y_valid.shape)\n",
    "pred_test_all = np.zeros(y_test.shape)\n",
    "\n",
    "for store in range(10):\n",
    "\n",
    "    print('-------', 'store', store, '-------')\n",
    "    \n",
    "    es = EarlyStopping(patience=5, verbose=1, min_delta=0.001, monitor='val_loss', mode='auto', restore_best_weights=True)\n",
    "\n",
    "    model = get_model()\n",
    "    model.fit([X_train_seq, X_train_lap, X_train_feat], y_train[:,store], epochs=100, batch_size=256, \n",
    "              validation_data=([X_valid_seq, X_valid_lap, X_valid_feat], y_test[:,store]), callbacks=[es], verbose=2)\n",
    "\n",
    "    pred_valid_all[:,store] = model.predict([X_valid_seq, X_valid_lap, X_valid_feat]).ravel()\n",
    "    pred_test_all[:,store] = model.predict([X_test_seq, X_test_lap, X_test_feat]).ravel()\n",
    "\n",
    "\n",
    "pred_valid_all = scaler_seq.inverse_transform(pred_valid_all)\n",
    "reverse_valid = scaler_seq.inverse_transform(y_valid)\n",
    "pred_test_all = scaler_seq.inverse_transform(pred_test_all)\n",
    "reverse_test = scaler_seq.inverse_transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "### RMSE ON TEST DATA ###\n",
    "\n",
    "error = {}\n",
    "\n",
    "for store in range(10):\n",
    "    \n",
    "    error[store] = np.sqrt(mean_squared_error(reverse_test[:,store], pred_test_all[:,store]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### PLOT RMSE ###\n",
    "\n",
    "plt.figure(figsize=(14,5))\n",
    "plt.bar(range(10), error.values())\n",
    "plt.xticks(range(10), ['store_'+str(s) for s in range(10)])\n",
    "plt.ylabel('error')\n",
    "np.set_printoptions(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "### UTILITY FUNCTION TO PLOT PREDICTION ###\n",
    "\n",
    "def plot_predictions(y_true, y_pred, store, item):\n",
    "    \n",
    "    y_true = y_true.reshape(50,-1,10)\n",
    "    y_pred = y_pred.reshape(50,-1,10)\n",
    "    \n",
    "    plt.plot(y_true[item,:,store], label='true')\n",
    "    plt.plot(y_pred[item,:,store], label='prediction')\n",
    "    plt.title(f\"store: {store} item: {item}\"); plt.legend()\n",
    "    plt.ylabel('sales'); plt.xlabel('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,5))\n",
    "plot_predictions(reverse_test, pred_test_all, 7,0)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:tensorflow2]",
   "language": "python",
   "name": "conda-env-tensorflow2-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
